Bagging and Boosting statistical machine translation systems

Artificial Intelligence

Volumn 195, Issue , 2013, Pages 496-527

  Xiao, Tong ^a   Zhu, Jingbo ^a   Liu, Tongran ^b  

^a NORTHEASTERN UNIVERSITY   (China)

^b INSTITUTE OF PSYCHOLOGY   (China)

Author keywords

Ensemble learning; Statistical machine translation; System combination

Indexed keywords

COMPUTATIONAL LINGUISTICS; COMPUTER AIDED LANGUAGE TRANSLATION; ENGINES; LEARNING ALGORITHMS; LINGUISTICS; SPEECH TRANSMISSION;

ENSEMBLE LEARNING; STATE-OF-THE-ART SYSTEM; STATISTICAL MACHINE TRANSLATION; STATISTICAL MACHINE TRANSLATION SYSTEM; SYSTEM COMBINATION; TRADITIONAL APPROACHES; TRANSLATION ENGINES; TRANSLATION SYSTEMS;

HIERARCHICAL SYSTEMS;

EID: 84884907416     PISSN: 00043702     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artint.2012.11.005     Document Type: Article

References (113)

1. N. Abe, H. Mamitsuka, Query learning strategies using Boosting and Bagging, in: Proceedings International Conference on Machine Learning (ICML), Wisconsin, USA, 1998, pp. 1-9.
2. S. Abney, R. Schapire, Y. Singer, Boosting applied to tagging and PP attachment, in: Proceedings Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC), Maryland, USA, 1999, pp. 38-45.
3. E. Alpaydin, Introduction to Machine Learning, Adaptive Computation and Machine Learning, The MIT Press, Cambridge, 2010.
4. S. Banerjee, A. Lavie, METEOR: An automatic metric for MT evaluation with improved correlation with human judgments, in: Proceedings ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization, Edinburgh, Scotland, 2005, pp. 65-72.
5. R.E. Banfield, L.O. Hall, K.W. Bowyer, W.P. Kegelmeyer, A comparison of decision tree ensemble creation techniques, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (1) (2007) 173-180. (Pubitemid 46415955)
6. S. Bangalore, G. Bordel, G. Riccardi, Computing consensus translation from multiple machine translation systems, in: Proceedings Automatic Speech Recognition and Understanding Workshop (ASRU), Madonna di Campiglio, Italy, 2001, pp. 351-354.
7. E. Bauer, R. Kohavi, An empirical comparison of voting classification algorithms: Bagging, Boosting, and variants, Machine Learning 36 (1/2) (1999) 105-139.
8. A.L. Berger, S.A. Pietra, V.J. Pietra, A maximum entropy approach to natural language processing, Computational Linguistics 22 (1) (1996) 39-71.
9. P.J. Bickel, K.A. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics, Holden-Day Inc., Oakland, CA, USA, 1977.
10. L. Breiman, Bagging predictors, Machine Learning 24 (2) (1996) 123-140. (Pubitemid 126724382)
11. L. Breiman, Heuristics of instability and stabilization in model selection, Annals of Statistics 24 (1996) 2350-2383.
12. L. Breiman, Heuristics of instability and stabilization in model selection, Technical report no. 416, Department of Statistics, University of California, Berkeley, CA, 1994.
13. P.F. Brown, S.A. Pietra, V.J. Pietra, R.L. Mercer, The mathematics of statistical machine translation: Parameter estimation, Computational Linguistics 19 (2) (1993) 263-311.
14. D. Chiang, A hierarchical phrase-based model for statistical machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Ann Arbor, MI, 2005, pp. 263-270.
15. D. Chiang, Hierarchical phrase-based translation, Computational Linguistics 33 (2) (2007) 201-228.
16. D. Chiang, S. DeNeefe, Y.S. Chan, H.T. Ng, Decomposability of translation metrics for improved evaluation and efficient algorithms, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, HI, 2008, pp. 610-619.
17. D. Chiang, Y. Marton, P. Resnik, Online large-margin training of syntactic and structural translation features, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, HI, 2008, pp. 224-233.
18. J.H. Clark, C. Dyer, A. Lavie, N.A. Smith, Better hypothesis testing for statistical machine translation: Controlling for optimizer instability, in: Proceedings Annual meeting of the Association for Computational Linguistics (ACL), Portland, OR, USA, 2011, pp. 176-181.
19. M. Collins, Y. Singer, Unsupervised models for named entity classification, in: Proceedings Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC), College Park, MD, USA, 1999, pp. 100-110.
20. B. Cowan, I. Kučerová, M. Collins, A discriminative model for tree-to-tree translation, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Sydney, Australia, 2006, pp. 232-241.
21. T.G. Dietterich, Machine learning research: Four current directions, AI Magazine 18 (4) (1997) 97-136. (Pubitemid 127684659)
22. T.G. Dietterich, An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, Boosting and randomization, Machine Learning 40 (2) (2000) 1-19.
23. Y. Ding, M. Palmer, Machine translation using probabilistic synchronous dependency insertion grammars, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Ann Arbor, MI, 2005, pp. 541-548.
24. G. Doddington, Automatic evaluation of machine translation quality using n-gram co-occurrence statistics, in: Proceedings ARPA Workshop on Human Language Technology, Santa Monica, CA, USA, 2002, pp. 1-8.
25. H. Drucker, C. Cortes, L.D. Jackel, Y. Lecun, V. Vapnik, Boosting and other machine learning algorithms, in: Proceedings International Conference on Machine Learning (ICML), New Brunswick, NJ, USA, 1994, pp. 53-61.
26. N. Duan, M. Li, T. Xiao, M. Zhou, The feature subspace method for SMT system combination, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Suntec, Singapore, 2009, pp. 1096-1104.
27. N. Duan, M. Li, D. Zhang, M. Zhou, Mixture model-based minimum Bayes risk decoding using multiple machine translation systems, in: Proceedings International Conference on Computational Linguistics (COLING), Beijing, China, 2010, pp. 313-321.
28. N. Duan, H. Sun, M. Zhou, Translation model generalization using probability averaging for machine translation, in: Proceedings International Conference on Computational Linguistics (COLING), Beijing, China, 2010, pp. 304-312.
29. N. Duffy, D. Helmbold, Potential boosters? in: Proceedings Advances in Neural Information Processing Systems (NIPS), Granada, Spain, 1999, pp. 258-264.
30. K. Duh, K. Kirchhoff, Beyond log-linear models: Boosted minimum error rate training for N-best Re-ranking, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Columbus, OH, 2008, pp. 37-40.
31. K. Duh, K. Sudoh, H. Tsukada, H. Isozaki, M. Nagata, N-best reranking by multitask learning, in: Proceedings the Fifth Workshop on Statistical Machine Translation and METRICS MATR, Uppsala, Sweden, 2010, pp. 375-383.
32. J. Eisner, Learning non-isomorphic tree mappings for machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Sapporo, Japan, 2003, pp. 205-208.
33. M. Federico, M. Cettolo, Efficient handling of n-gram language models for statistical machine translation, in: Proceedings the Second Workshop on Statistical Machine Translation, Prague, Czech Republic, 2007, pp. 88-95.
34. Y. Feng, Y. Liu, H. Mi, Q. Liu, Y. Lü, Lattice-based system combination for statistical machine translation, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Suntec, Singapore, 2009, pp. 1105-1113.
35. Y. Freund, Boosting a weak learning algorithm by majority, Information and Computation 121 (2) (1995) 256-285.
36. Y. Freund, R. Schapire, Experiments with a new boosting algorithm, in: Proceedings International Conference on Machine Learning (ICML), Bari, Italy, 1996, pp. 148-156.
37. Y. Freund, R. Schapire, A decision-theoretic generalization of on-line learning and an application to Boosting, Journal of Computer and System Sciences 55 (1) (1997) 119-139. (Pubitemid 127433398)
38. J.H. Friedman, On bias, variance, 0/1-loss, and the curse-of- dimensionality, Data Mining and Knowledge Discovery 1 (1997) 55-77. (Pubitemid 127721236)
39. M. Galley, J. Graehl, K. Knight, D. Marcu, S. DeNeefe, W. Wang, I. Thayer, Scalable inferences and training of context-rich syntax translation models, in: Proceedings Annual meeting of the Association for Computational Linguistics (ACL), Sydney, Australia, 2006, pp. 961-968.
40. M. Galley, C.D. Manning, A simple and effective hierarchical phrase reordering model, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Hawaii, USA, 2008, pp. 848-856.
41. V. Goel, W. Byrne, Minimum Bayes-risk automatic speech recognition, Computer Speech and Language 14 (2) (2000) 115-135.
42. J. Gonzalez-Rubio, A. Juan, F. Casacuberta, Minimum Bayes-risk system combination, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Portland, OR, USA, 2011, pp. 1268-1277.
43. L. Hansen, P. Salamon, Neural network ensembles, IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (10) (1990) 993-1001.
44. S. Hasan, R. Zens, H. Ney, Are very large n -best lists useful for SMT? in: Proceedings Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), Rochester, USA, 2007, pp. 57-60.
45. S. Hashem, Optimal linear combinations of neural networks, Neural Networks 10 (4) (1997) 599-614. (Pubitemid 27357174)
46. X. He, M. Yang, J. Gao, P. Nguyen, R. Moore, Indirect HMM based hypothesis alignment for combining outputs from machine translation systems, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, HI, 2008, pp. 98-107.
47. K. Heafield, A. Lavie, Combining machine translation output with open source: The Carnegie Mellon multi-engine machine translation scheme, The Prague Bulletin of Mathematical Linguistics 93 (2010) 27-36.
48. J.C. Henderson, E. Brill, Bagging and Boosting a Treebank Parser, in: Proceedings North American Chapter of the Association for Computational Linguistics (NAACL), Seattle, WA, USA, 2000, pp. 34-41.
49. A.S. Hildebrand, S. Vogel, Combination of machine translation systems via hypothesis selection from combined n-best lists, in: Proceedings Conference of the Association for Machine Translation in the Americas (AMTA), Waikiki, HI, 2008, pp. 254-261.
50. T.K. Ho, The random subspace method for constructing decision forests, IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (8) (1998) 832-844. (Pubitemid 128741345)
51. L. Huang, D. Chiang, Forest rescoring: Faster decoding with integrated language models, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic, 2007, pp. 144-151.
52. A.K. Jain, R.P. Duin, J. Mao, Statistical pattern recognition: A review, IEEE Transactions on Pattern Analysis and Machine Intelligence 22 (1) (2000) 4-37. (Pubitemid 32132953)
53. D. Klein, C. Manning, Parsing and hypergraphs, in: Proceedings the Seventh International Workshop on Parsing Technologies (IWPT), Beijing, China, 2001, pp. 351-372.
54. K. Knight, Decoding complexity in word-replacement translation models, Computational Linguistics 25 (4) (1999) 607-615.
55. P. Koehn, Statistical significance tests for machine translation evaluation, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Barcelona, Spain, 2004, pp. 388-395.
56. P. Koehn, Statistical Machine Translation, Cambridge University Press, UK, 2010.
57. P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, E. Herbst Moses, Open source toolkit for statistical machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL) Companion Volume Proceedings of the Demo and Poster Sessions, Prague, Czech Republic, 2007, pp. 177-180.
58. P. Koehn, F. Och, D. Marcu, Statistical phrase-based translation, in: Proceedings Human Language Technology and Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), Edmonton, USA, 2003, pp. 48-54.
59. A. Krogh, J. Vedelsby, Neural network ensembles, cross validation, and active learning, in: Proceedings Advances in Neural Information Processing Systems (NIPS), The MIT Press, USA, 1994, pp. 231-238.
60. S. Kumar, W. Byrne, Minimum Bayes-risk decoding for statistical machine translation, in: Proceedings Joint Conference on Human Language Technologies and the Annual Meeting of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL), Boston, MA, USA, 2004, pp. 169-176.
61. S. Kumar, W. Macherey, C. Dyer, F. Och, Efficient minimum error rate training and minimum Bayes-risk decoding for translation hypergraphs and lattices, in: Proceedings Annual Meeting of the Association for Computational Linguistics and Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL/IJCNLP), Suntec, Singapore, 2009, pp. 163-171.
62. A. Lagarda, F. Casacuberta, Applying Boosting to statistical machine translation, in: Proceedings Annual Meeting of European Association for Machine Translation (EAMT), Hamburg, Germany, 2008, pp. 88-96.
63. M. Li, N. Duan, D. Zhang, C. Li, M. Zhou, Collaborative decoding: Partial hypothesis re-ranking using translation consensus between decoders, in: Proceedings Annual Meeting of the Association for Computational Linguistics and Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL/IJCNLP), Suntec, Singapore, 2009, pp. 585-592.
64. C. Li, X. He, Y. Liu, N. Xi, Incremental HMM alignment for MT system combination, in: Proceedings Annual Meeting of the Association for Computational Linguistics and Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL/IJCNLP), Suntec, Singapore, 2009, pp. 949-957.
65. P. Liang, A. Bouchard-Côté, D. Klein, B. Taskar, An end-to-end discriminative approach to machine translation, in: Proceedings International Conference on Computational Linguistics and Annual Meeting of the Association for Computational Linguistics (COLING/ACL), Sydney, Australia, 2006, pp. 104-111.
66. Y. Liu, Q. Liu, S. Lin, Tree-to-string alignment template for statistical machine translation, in: Proceedings International Conference on Computational Linguistics and Annual Meeting of the Association for Computational Linguistics (COLING/ACL), Sydney, Australia, 2006, pp. 609-616.
67. Y. Liu, H. Mi, Y. Feng, Q. Liu, Joint decoding with multiple translation models, in: Proceedings Annual Meeting of the Association for Computational Linguistics and Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL/IJCNLP), Suntec, Singapore, 2009, pp. 576-584.
68. A. Lopez, Statistical machine translation, ACM Computing Surveys 40 (3) (2008) 1-49.
69. W. Macherey, F. Och, An empirical study on computing consensus translations from multiple machine translation systems, in: Proceedings Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP/CoNLL), Prague, Czech Republic, 2007, pp. 986-995.
70. D. Marcu, W. Wang, A. Echihabi, K. Knight, SPMT: Statistical machine translation with syntactified target language phrases, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Sydney, Australia, 2006, pp. 44-52.
71. S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman & Hall/CRC Machine Learning & Pattern Recognition, CRC Press, Boca Raton, 2009.
72. L. Mason, J. Baxter, P. Bartlett, M. Frean, Functional Gradient Techniques for Combining Hypotheses, Advances in Large Margin Classifiers, The MIT Press, USA, 1999.
73. L. Mason, J. Baxter, P. Bartlett, M. Frean, Boosting algorithms as gradient descent, in: Proceedings Advances in Neural Information Processing Systems (NIPS), Denver, CO, USA, 2000, pp. 512-518.
74. S. Matsoukas, A.I. Rosti, B. Zhang, Discriminative corpus weight estimation for machine translation, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Suntec, Singapore, 2009, pp. 708-717.
75. E. Matusov, G. Leusch, R.E. Banchs, N. Bertoldi, D. Déchelotte, M. Federico, M. Kolss, Y. Lee, J.B. Mariño, M. Paulik, S. Roukos, H. Schwenk, H. Ney, System combination for machine translation of spoken and written language, IEEE Transactions on Audio Speech and Language Processing 16 (7) (2008) 1222-1237.
76. E. Matusov, N. Ueffing, H. Ney, Computing consensus translation from multiple machine translation systems using enhanced hypotheses alignment, in: Proceedings Annual Conference of the European Chapter of the Association for Computational Linguistics (EACL), Trento, Italy, 2006, pp. 33-40.
77. P. Nguyen, M. Mahajan, X. He, Training non-parametric features for statistical machine translation, in: Proceedings the Second Workshop on Statistical Machine Translation, Prague, Czech Republic, 2007, pp. 72-79.
78. S. Nießen, F. Och, G. Leusch, H. Ney, An evaluation tool for machine translation: Fast evaluation for machine translation research, in: Proceedings the Second International Conference on Language Resources and Evaluation (LREC), Athens, Greece, 2000, pp. 39-45.
79. F. Och, Minimum error rate training in statistical machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Sapporo, Japan, 2003, pp. 160-167.
80. F. Och, D. Gildea, S. Khudanpur, A. Sarkar, K. Yamada, A. Fraser, S. Kumar, L. Shen, D. Smith, K. Eng, V. Jain, Z. Jin, D. Radev, A smorgasbord of features for statistical machine translation, in: Proceedings Joint Conference on Human Language Technologies and the Annual Meeting of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL), New York City, USA, 2004, pp. 161-168.
81. F. Och, H. Ney, Discriminative training and maximum entropy models for statistical machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA, USA, 2002, pp. 295-302.
82. D. Opitz, R. Maclin, Popular ensemble methods: An empirical study, Journal of Artificial Intelligence Research 11 (1999) 16-98.
83. D. Opitz, J. Shavlik, Actively searching for an effective neural-network ensemble, Connection Science 8 (3/4) (1996) 337-353. (Pubitemid 126400569)
84. K. Papineni, S. Roukos, T. Ward, W. Zhu, BLEU: A method for automatic evaluation of machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, PA, USA, 2002, pp. 311-318.
85. A. Pauls, D. Klein, Faster and smaller n -gram language models, in: Proceedings Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL-HLT), Portland, OR, USA, 2011, pp. 258-267.
86. A. Ratnaparkhi, Maximum entropy models for natural language ambiguity resolution, Ph.D. thesis, University of Pennsylvania, PA, 1998.
87. L. Rokach, Ensemble-based classifiers, Artificial Intelligence Review 33 (2010) 1-39.
88. A. Rosti, S. Matsoukas, R. Schwartz, Improved word-level system combination for machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Prague, Czech Republic, 2007, pp. 312-319.
89. A. Rosti, B. Xiang, S. Matsoukas, R. Schwartz, N. Ayan, B.J. Dorr, Combining outputs from multiple machine translation systems, in: Proceedings Human Language Technology and Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), Rochester, New York, 2007, pp. 228-235.
90. S. Russell, P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall, NJ, 1995.
91. R.E. Schapire, The strength of weak learnability, Machine Learning 5 (2) (1990) 197-227.
92. R.E. Schapire, The Boosting approach to machine learning: An overview, in: D.D. Denison, M.H. Hansen, C. Holmes, B. Mallick, B. Yu (Eds.), Nonlinear Estimation and Classification, Springer, New York, 2003.
93. R.E. Schapire, Y. Freund, P. Bartlett, W.S. Lee, Boosting the margin: A new explanation for the effectiveness of voting methods, The Annals of Statistics 26 (5) (1998) 1651-1686. (Pubitemid 128376902)
94. R.E. Schapire, Y. Singer, BoosTexter: A boosting-based system for text categorization, Machine Learning 39 (2/3) (2000) 135-168. (Pubitemid 30594821)
95. M. Sebban, R. Nock, S. Lallich, Boosting neighborhood-based classifiers, in: Proceedings International Conference on Machine Learning (ICML), Williamstown, MA, USA, 2001, pp. 505-512.
96. M. Sebban, R. Nock, S. Lallich, Stopping criterion for Boosting-based data reduction techniques: From binary to multiclass problems, Journal of Machine Learning Research 3 (2002) 863-885.
97. L. Shen, A. Sarkar, F. Och, Discriminative reranking for machine translation, in: Proceedings Joint Conference on Human Language Technologies and the Annual Meeting of the North American Chapter of the Association of Computational Linguistics (HLT/NAACL), Boston, MA, USA, 2004, pp. 177-184.
98. K.C. Sim, W. Byrne, M. Gales, H. Sahbi, P. Woodland, Consensus network decoding for statistical machine translation system combination, in: Proceedings International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Honolulu, HI, 2007, pp. 105-108.
99. M. Skurichina, Stabilizing weak classifiers: Regularization and combining techniques in discriminant analysis, Ph.D. thesis, Delft University of Technology, Delft, 2001.
100. M. Skurichina, R. Duin Bagging, Boosting and the random subspace method for linear classifiers, Pattern Analysis and Applications 5 (2) (2002) 1211-1235.
101. N.A. Smith, Novel estimation methods for unsupervised discovery of latent structure in natural language text, Ph.D. thesis, Johns Hopkins University, Baltimore, MD, 2006.
102. M. Snover, B. Dorr, R. Schwartz, L. Micciulla, J. Makhoul, A study of translation edit rate with targeted human annotation, in: Proceedings Conference of the Association for Machine Translation in the Americas (AMTA), Cambridge, MA, USA, 2006, pp. 223-231.
103. R.W. Tromble, S. Kumar, F. Och, W. Macherey, Lattice minimum Bayes-risk decoding for statistical machine translation, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Honolulu, HI, 2008, pp. 620-629.
104. S. Vemulapalli, X. Luo, J.F. Pitrelli, I. Zitouni, Using Bagging and Boosting techniques for improving coreference resolution, Informatica 34 (2010) 111-118.
105. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, San Francisco, CA, 2005.
106. D.H. Wolpert, Stacked generalization, Neural Networks 5 (2) (1992) 241-259.
107. T. Xiao, J, Zhu, H. Zhang, Q. Li, NiuTrans: An open source toolkit for phrase-based and syntax-based machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL) System Demonstrations, Jeju Island, Korea, 2012, pp. 19-24.
108. T. Xiao, M. Li, D. Zhang, J. Zhu, M. Zhou, Better synchronous binarization for machine translation, in: Proceedings Conference on Empirical Methods in Natural Language Processing (EMNLP), Suntec, Singapore, 2009, pp. 362-370.
109. T. Xiao, J. Zhu, M. Zhu, H. Wang, Boosting-based System combination for machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Uppsala, Sweden, 2010, pp. 739-748.
110. D. Xiong, Q. Liu, S. Lin, Maximum entropy based phrase reordering model for statistical machine translation, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Sydney, Australia, 2006, pp. 521-528.
111. H. Zhang, D. Gildea, Efficient multi-pass decoding for synchronous context free grammars, in: Proceedings Annual Meeting of the Association for Computational Linguistics (ACL), Columbus, OH, 2008, pp. 209-217.
112. H. Zhang, L. Huang, D. Gildea, K. Knight, Synchronous binarization for machine translation, in: Proceedings Human Language Technology and Annual Conference of the North American Chapter of the Association for Computational Linguistics (HLT/NAACL), New York City, USA, 2006, pp. 256-263.
113. R. Zhang, K. Yasuda, E. Sumita, Improved statistical machine translation by multiple Chinese word segmentation, in: Proceedings the Third Workshop on Statistical Machine Translation, Columbus, OH, 2008, pp. 216-223.